Conjoint Design Expert Agent Skill

Design conjoint experiments: attributes, power, AMCE/AMIE estimation.

Context cost
11.3k tokensestimated from the bundle, loaded when it triggers
Bundle
2 files · 45.1 kBtext throughout, nothing executable
Licence
Source-availablethe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Design conjoint experiments: attributes, power, AMCE/AMIE estimation.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data science

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md38.7 kB · preview
--- name: conjoint-design argument-hint: "[describe your design question or paste attribute table]" ---
7# Conjoint Design Expert
8
9## Instructions
10
11> Worked example (attribute table → power calculation → PAP tier assignment): see reference/example.md.
12
13### 1. Attribute Architecture
14- **Orthogonality:** Ensure every attribute is independent of every other attribute to allow for the estimation of causal effects for each component.
15- **Randomization of Order:** Order attributes randomly at the *respondent level* (not the task level) to prevent "primacy" or "recency" effects while avoiding the cognitive overload of finding information in different orders across tasks (Stantcheva 2023). A specific logical flow may override this if theoretically required.
16- **D-Optimal Designs:** Consider D-optimal or constrained randomization schemes rather than pure randomization. D-optimal designs choose the sets of administered conditions that maximize statistical power and may be preferable when the number of possible attribute combinations is large relative to the sample size (Auspurg & Hinz 2015; Stantcheva 2023).
17- **Attribute Density:** Monitor for respondent fatigue. Stefanelli and Lukac (2020) cite evidence that conjoint results remain stable with up to 10 attributes; Bansak et al. (2018) find that response quality does not degrade with up to 30 *tasks* on MTurk, and Bansak et al. (2021, "Beyond the Breaking Point") extend this to the *attribute* dimension, reporting stability at many attributes. These are the canonical sources for the task- and attribute-count claims respectively. Still evaluate whether the complexity of the levels increases cognitive load beyond the attribute count alone.
18- **Nested/Constrained Randomization:** Not all attributes need to be fully crossed. When ecological validity demands it, certain attribute levels can be linked or nested within other attributes (e.g., origin countries nested within policy domain). This is acceptable when: (a) the nesting is theoretically justified, (b) the primary attributes of interest remain fully independently randomized, and (c) the analyst acknowledges that nested attributes cannot be cleanly separated from their parent attribute. See Auspurg & Hinz (2015) on restricted randomization in factorial surveys.
19- **Attribute-Level Restrictions:** Implausible combinations can be excluded when they would confuse respondents or produce artifactual responses, but this is a judgment call, not a mandate. Eye-tracking evidence from Bansak & Jenke (2025) shows that odd (incongruent or nonsensical) attribute combinations have minimal, inconsistent effects on respondent attention, search, and choice, so decisions to include or exclude them should be driven primarily by statistical, substantive, and theoretical considerations (e.g., whether the randomization distribution should reflect a real-world target distribution per De la Cuesta, Egami & Imai 2022) rather than by assumed cognitive-burden concerns. Document all restrictions in the pre-analysis plan.
20- **Medium-Level Specificity:** Attribute levels should be concrete enough to be meaningful but not so specific that they introduce unintended confounds. Describe treatments at a "medium level of specificity" -- "fully described but not overly described" (Sniderman 2018). Avoid vague descriptions (e.g., "a policy that helps the economy") and overly narrow ones (e.g., "a $2.3B infrastructure bill for Route 95 in Pennsylvania").
21
22### 2. Statistical Power and Error Logic
23- **Effective N (N_eff):** Calculate sample size based on (Respondents $\times$ Tasks $\times$ Profiles). Throughout this section, N_eff refers to this effective number of profile evaluations. However, respondents and tasks are *not interchangeable* -- adding respondents improves precision more than adding tasks per respondent due to within-respondent correlation. When in doubt, prioritize more respondents over more tasks (Stefanelli and Lukac 2020).
24- **Closed-Form Formula:** The standard error of an AMCE is approximately: SE = $\sqrt{\text{Var}(Y) \times L / N_{\text{eff}}}$, where $L$ is the number of levels for the attribute and N_eff is as defined above (Schuessler and Freitag 2020). This provides a quick diagnostic for whether precision is adequate.
25- **Interaction Power:** Estimating interaction effects requires approximately *twice* the sample size needed for main AMCEs, in the canonical balanced two-level-by-two-level case; the exact multiplier scales with the number of levels on each interacting attribute. The standard error of an interaction coefficient is approximately $\sqrt{2}$ times the SE of the corresponding main effect in that balanced case (Schuessler and Freitag 2020). Budget accordingly when interaction hypotheses are confirmatory.
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md5,328 words
Files2
LicenceSource-available
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈50
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
11,225
on trigger
The instruction body and 1 supporting file, read only when the skill fires.
5.6%
of a 200k window
Ten skills this size would take about 56% of the window before you open a file.
050k100k150k200k context window

11.3k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

2 files, 45.1 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md38.7 kB
  • reference/example.md6.4 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 2 files you can review in full before installing. The Source-available licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Conjoint Design Expert · 11.3k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/conjoint-design-expert/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/conjoint-design-expert" ] && { [ ! -e .claude/skills/conjoint-design-expert ] || { mv .claude/skills/conjoint-design-expert "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/conjoint-design-expert" .claude/skills/ && echo "Installed .claude/skills/conjoint-design-expert"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add brycewang-stanford/conjoint-design-expert

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/conjoint-design-expert/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/conjoint-design-expert/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/conjoint-design-expert" ] && { [ ! -e ~/.claude/skills/conjoint-design-expert ] || { mv ~/.claude/skills/conjoint-design-expert "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/conjoint-design-expert" ~/.claude/skills/ && echo "Installed ~/.claude/skills/conjoint-design-expert" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/conjoint-design-expert/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as conjoint-design in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, conjoint-design-expert.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencebrycewang-stanford/conjoint-design-expert

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

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